Performance analysis roundtrip: automatic generation of performance models and results feedback using cross-model trace links
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper proposes an approach for performance analysis roundtrip in the context of model-driven engineering (MDE) of real-time distributed and embedded systems. The starting point is a UML software model with MARTE performance annotations, such as performance requirements and resource demands. The source software model is automatically transformed into a Layered Queueing Network (LQN) performance model. We developed the transformation with Epsilon, a family of languages for model-to-model transformation, model validation and model management. Using specialized languages helped us create a more compact transformation, easier to understand and maintain than transformations developed with general purpose languages, such as Java. Beside the performance model, the transformation also generates a traceability model containing trace links between mapped elements of the software and performance model. After solving the performance model with an existing solver, the performance results are fed back to the software model by following in reverse the cross-model trace links. The software developers can see the performance results as MARTE stereotype attributes, using a standard UML editor. The approach is illustrated by applying it to an e-commerce application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it